Real-Time Anomaly Detection in FMCW Radar Systems Using Hybrid Autoencoder-LSTM Algorithm

Abstract

Frequency-Modulated Continuous-Wave (FMCW) radar systems are widely used in autonomous vehicles, aerospace, defense, industrial automation, and surveillance applications due to their ability to provide accurate range, velocity, and object detection. However, radar performance can be affected by anomalies caused by hardware faults, environmental interference, signal noise, and operational irregularities. Traditional detection methods based on thresholding and statistical analysis often fail to identify complex and time-dependent anomalies effectively. This research paper proposes a hybrid deep learning framework using Autoencoder and Long Short-Term Memory (LSTM) algorithms for anomaly detection in FMCW radar signals. The Autoencoder extracts important signal features and detects abnormal patterns through reconstruction error, while the LSTM model captures temporal dependencies in sequential radar data. The dataset is preprocessed using normalization, noise reduction, and segmentation techniques before model training. The proposed model is evaluated using accuracy, precision, recall, F1-score, confusion matrix, and training-validation performance. Experimental results show that the proposed Hybrid Autoencoder-LSTM framework achieves an overall accuracy of 94.18%, demonstrating reliable detection of normal and anomalous radar signal patterns. The results confirm that the hybrid model performs better than conventional machine learning methods by improving detection accuracy, robustness, and real-time applicability. The proposed system can support fault detection, predictive maintenance, and intelligent monitoring in FMCW radar-based applications.

Introduction

The paper presents a hybrid deep learning framework (Autoencoder + LSTM) for anomaly detection in Frequency-Modulated Continuous-Wave (FMCW) radar systems used in autonomous vehicles, aerospace, industrial automation, and defense applications. The main goal is to detect radar anomalies caused by hardware faults, noise, interference, or environmental disturbances, which can degrade safety and performance in critical systems.

The proposed method combines an Autoencoder to learn normal radar signal patterns through reconstruction, and an LSTM network to capture temporal dependencies in time-series radar data. When radar behavior deviates from normal patterns, reconstruction error increases, enabling anomaly detection.

The model is trained on large publicly available radar datasets and evaluated using metrics such as accuracy, precision, recall, F1-score, and ROC analysis. Experimental results show strong performance, achieving around 94–95% accuracy, and outperforming traditional methods like Random Forest and SVM as well as standalone deep learning models.

The findings show that hybrid architectures are more effective because they jointly model spatial features and temporal behavior in radar signals. The system is also more robust in noisy and dynamic environments compared to conventional approaches.

Conclusion

This research presented a comprehensive and intelligent deep learning-based framework for real-time anomaly detection in Frequency-Modulated Continuous-Wave (FMCW) radar systems using a hybrid Autoencoder-LSTM architecture. The primary objective of the study was to address the limitations of conventional anomaly detection techniques that rely heavily on threshold-based monitoring, statistical analysis, and handcrafted feature engineering. Traditional approaches often fail to identify subtle signal irregularities, evolving hardware degradation, environmental disturbances, and dynamic operational anomalies that commonly occur in modern radar systems. To overcome these challenges, the proposed framework integrated the spatial feature extraction capability of Autoencoders with the temporal sequence learning capability of Long Short-Term Memory (LSTM) networks, thereby enabling robust and adaptive anomaly detection in sequential radar signal data. Experimental evaluation demonstrated that the proposed Hybrid Autoencoder-LSTM framework achieved an overall anomaly detection accuracy of 95.13%, significantly outperforming conventional machine learning approaches such as Support Vector Machine and Random Forest classifiers. In addition to high accuracy, the model achieved balanced precision, recall, and F1-score values across all operational categories, indicating stable and unbiased classification performance. Confusion matrix analysis further confirmed strong class separability and minimal misclassification rates, while training and validation convergence curves demonstrated effective generalization capability and minimal overfitting behavior. These findings validate the robustness and scalability of the proposed framework for real-time radar monitoring applications. In conclusion, this study establishes that hybrid Autoencoder-LSTM architectures provide an effective, scalable, and intelligent solution for real-time anomaly detection in FMCW radar systems. By combining spatial reconstruction learning with temporal sequence modeling, the proposed framework significantly improves anomaly detection accuracy, operational reliability, predictive maintenance capability, and system safety. The research contributes both academically and practically toward the advancement of AI-driven radar monitoring systems and highlights the growing importance of deep learning technologies in next-generation intelligent sensing applications.

References

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Copyright

Copyright © 2026 Rahul Jatav, Prof. Prakash Saxena. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.